Install and load bibliometrix R-package
# Stable version from CRAN (Comprehensive R Archive Network)
# if you need to execute the code, remove # from the beginning of the next line
# install.packages("bibliometrix")
# Most updated version from GitHub
# if you need to execute the code, remove # from the beginning of the next lines
# install.packages("devtools")
# devtools::install_github("massimoaria/bibliometrix")
library(bibliometrix)
package ‘bibliometrix’ was built under R version 3.5.2To cite bibliometrix in publications, please use:
Aria, M. & Cuccurullo, C. (2017) bibliometrix: An R-tool for comprehensive science mapping analysis, Journal of Informetrics, 11(4), pp 959-975, Elsevier.
http:\\www.bibliometrix.org
To start with the shiny web-interface, please digit:
biblioshiny()
Section 1: Descriptive Analysis
Although bibliometrics is mainly known for quantifying the scientific production and measuring its quality and impact, it is also useful for displaying and analysing the intellectual, conceptual and social structures of research as well as their evolution and dynamical aspects.
In this way, bibliometrics aims to describe how specific disciplines, scientific domains, or research fields are structured and how they evolve over time. In other words, bibliometric methods help to map the science (so-called science mapping) and are very useful in the case of research synthesis, especially for the systematic ones.
Bibliometrics is an academic science founded on a set of statistical methods, which can be used to analyze scientific big data quantitatively and their evolution over time and discover information. Network structure is often used to model the interaction among authors, papers/documents/articles, references, keywords, etc.
Bibliometrix is an open-source software for automating the stages of data-analysis and data-visualization. After converting and uploading bibliographic data in R, Bibliometrix performs a descriptive analysis and different research-structure analysis.
Descriptive analysis provides some snapshots about the annual research development, the top “k” productive authors, papers, countries and most relevant keywords.
Main findings about the collection
#options(width=160)
results <- biblioAnalysis(M)
summary(results, k=10, pause=F, width=130)
Main Information about data
Documents 237
Sources (Journals, Books, etc.) 100
Keywords Plus (ID) 207
Author's Keywords (DE) 421
Period 2000 - 2019
Average citations per documents 16.09
Authors 368
Author Appearances 745
Authors of single-authored documents 11
Authors of multi-authored documents 357
Single-authored documents 12
Documents per Author 0.644
Authors per Document 1.55
Co-Authors per Documents 3.14
Collaboration Index 1.59
Document types
ARTICLE 154
ARTICLE, BOOK CHAPTER 9
ARTICLE, DATA PAPER 1
ARTICLE, PROCEEDINGS PAPER 21
PROCEEDINGS PAPER 52
Annual Scientific Production
Annual Percentage Growth Rate 5.297827
Most Productive Authors
Top manuscripts per citations
Corresponding Author's Countries
SCP: Single Country Publications
MCP: Multiple Country Publications
Total Citations per Country
Most Relevant Sources
Most Relevant Keywords
plot(x=results, k=10, pause=F)





Most Cited References
CR <- citations(M, field = "article", sep = ";")
cbind(CR$Cited[1:20])
[,1]
GOLDEN BL, 1981, NETWORKS, V11, P305, DOI 10.1002/NET.3230110308. 144
GOLDEN BL, 1983, COMPUT OPER RES, V10, P47, DOI 10.1016/0305-0548(83)90026-6. 100
HERTZ A, 2000, OPER RES, V48, P129, DOI 10.1287/OPRE.48.1.129.12455. 88
LACOMME P, 2004, ANN OPER RES, V131, P159, DOI 10.1023/B:ANOR.0000039517.35989.6D. 88
BEULLENS P, 2003, EUR J OPER RES, V147, P629, DOI 10.1016/S0377-2217(02)00334-X. 82
BENAVENT E, 1992, NETWORKS, V22, P669, DOI 10.1002/NET.3230220706. 78
DROR M., 2000, ARC ROUTING THEORY S. 74
BELENGUER JM, 2003, COMPUT OPER RES, V30, P705, DOI 10.1016/S0305-0548(02)00046-1. 71
ULUSOY G, 1985, EUR J OPER RES, V22, P329, DOI 10.1016/0377-2217(85)90252-8. 71
BRANDAO J, 2008, COMPUT OPER RES, V35, P1112, DOI 10.1016/J.COR.2006.07.007. 70
EGLESE RW, 1994, DISCRETE APPL MATH, V48, P231, DOI 10.1016/0166-218X(92)00003-5. 60
LONGO H, 2006, COMPUT OPER RES, V33, P1823, DOI 10.1016/J.COR.2004.11.020. 56
EISELT HA, 1995, OPER RES, V43, P399, DOI 10.1287/OPRE.43.3.399. 48
TANG K, 2009, IEEE T EVOLUT COMPUT, V13, P1151, DOI 10.1109/TEVC.2009.2023449. 48
LI LYO, 1996, J OPER RES SOC, V47, P217, DOI 10.1057/JORS.1996.20. 47
HERTZ A, 2001, TRANSPORT SCI, V35, P425, DOI 10.1287/TRSC.35.4.425.10431. 45
BELENGUER JM, 2006, COMPUT OPER RES, V33, P3363, DOI 10.1016/J.COR.2005.02.009. 44
LACOMME P, 2001, LECT NOTES COMPUT SC, V2037, P473. 42
BALDACCI R, 2006, NETWORKS, V47, P52, DOI [10.1002/NET.20091, 10.1002/NET.20091]. 41
HIRABAYASHI R, 1992, ASIA PAC J OPER RES, V9, P155. 38
Section 2: The Intellectual Structure of the field - Co-citation Analysis
Citation analysis is one of the main classic techniques in bibliometrics. It shows the structure of a specific field through the linkages between nodes (e.g. authors, papers, journal), while the edges can be differently interpretated depending on the network type, that are namely co-citation, direct citation, bibliographic coupling. Please see Aria, Cuccurullo (2017).
Below there are three examples.
First, a co-citation network that shows relations between cited-reference works (nodes).
Second, a co-citation network that uses cited-journals as unit of analysis.
The useful dimensions to comment the co-citation networks are: (i) centrality and peripherality of nodes, (ii) their proximity and distance, (iii) strength of ties, (iv) clusters, (iiv) bridging contributions.
Third, a historiograph is built on direct citations. It draws the intellectual linkages in a historical order. Cited works of thousands of authors contained in a collection of published scientific articles is sufficient for recostructing the historiographic structure of the field, calling out the basic works in it.
Article (References) co-citation analysis
Plot options:
n = 50 (the funxtion plots the main 50 cited references)
type = “fruchterman” (the network layout is generated using the Fruchterman-Reingold Algorithm)
size.cex = TRUE (the size of the vertices is proportional to their degree)
size = 20 (the max size of vertices)
remove.multiple=FALSE (multiple edges are not removed)
labelsize = 0.7 (defines the size of vertex labels)
edgesize = 10 (The thickness of the edges is proportional to their strength. Edgesize defines the max value of the thickness)
edges.min = 5 (plots only edges with a strength greater than or equal to 5)
all other arguments assume the default values
NetMatrix <- biblioNetwork(M, analysis = "coupling", network = "references", sep = ";")
net=networkPlot(NetMatrix, n = 50, Title = "Citation Network", type = "fruchterman", size.cex=TRUE, size=20, remove.multiple=FALSE, labelsize=0.7,edgesize = 10, edges.min=5)
